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import gradio as gr
import numpy as np
js_update_tab_labels = """
async () => {
let el_list = document.getElementById("tab_labeler").getElementsByClassName("svelte-1kcgrqr")
let idx = (el_list[1].value === "LOADING") ? 1 : parseInt(el_list[1].value)
console.log(idx)
style_sheet = document.getElementById("tab_style")
style_sheet.innerHTML = ""
for (let i = 1; i <= idx; i++) {
style_sheet.innerHTML += "#result_tabs button.svelte-kqij2n:nth-child(" + i + "):before {content: 'Result " + i + "';}"
}
}
"""
table_headers = ["TOTAL", "START_FRAME", "END_FRAME", "DIR", "R", "THETA", "L", "TRAVEL"]
info_headers = [
"TOTAL_TIME", "DATE", "START", "END", "FRAME_RATE", "",
"TOTAL_FISH", "UPSTREAM_FISH", "DOWNSTREAM_FISH", "NONDIRECTIONAL_FISH", "",
"UPSTREAM_MOTION", "INTENSITY", "THRESHOLD", "WATER_TEMP", "",
]
max_tabs = 10
tabs = []
tab_parent = None
zip_out = None
def update_result(i, state, result, inference_handler):
# If index is larger than max_tabs, only add file to zip list
if i >= max_tabs:
return {
zip_out: gr.update(value=result["path_zip"])
}
# Check if inference is done
not_done = state['index'] < state['total']
annotation_avaliable = not (result["aris_input"][i] == None)
# Send update to UI, and to inference_handler to start next file inference
return {
zip_out: gr.update(value=result["path_zip"]),
tabs[i]['tab']: gr.update(),
tabs[i]['video']: gr.update(value=result["path_video"][i], visible=True),
tabs[i]['metadata']: gr.update(value=result["fish_info"][i], visible=True),
tabs[i]['table']: gr.update(value=result["fish_table"][i], visible=True),
tabs[i]['annotation_btn']: gr.update(visible=annotation_avaliable),
tab_parent: gr.update(selected=i),
inference_handler: gr.update(value = str(np.random.rand()), visible=not_done)
}
def Result_Gradio(prepare_annotation, components):
global tabs, tab_parent, zip_out
# Dummy element to call inference events, this also displays the inference progress
components['inference_handler'] = gr.Text(value=str(np.random.rand()), visible=False)
# Dummy element to call UI events
components['result_handler'] = gr.Text(value="LOADING", visible=False)
# Dummy element for updating tab titles
components['tab_labeler'] = gr.Text(value="", visible=False, elem_id="tab_labeler")
components['tab_labeler'].change(lambda x: x, None, None, _js=js_update_tab_labels)
with gr.Row():
components['cancelBtn'] = gr.Button("Cancel Inference", visible=False)
components['skipBtn'] = gr.Button("Skip this file", visible=False)
# List of all UI components that will recieve outputs from the result_handler
visual_components = []
# Zip file output
zip_out = gr.File(label="ZIP Output", interactive=False)
visual_components.append(zip_out)
# Create result tabs
tabs = []
with gr.Tabs(elem_id="result_tabs") as tab_parent:
visual_components.append(tab_parent)
# Create 'max_tab' tabs for showing result
for i in range(max_tabs):
with gr.Tab(label="", id=i, elem_id="result_tab"+str(i)) as tab:
with gr.Row():
# List of clip info (date, time, number of fish, temperature, etc.)
metadata_out = gr.Matrix(label="Info", interactive=False, headers=["Field", "Value"], datatype="markdown", visible=False, elem_id="marking_json")
# Annotated video
video_out = gr.Video(label='Annotated Video', interactive=False, visible=False)
# Table of found fish
table_out = gr.Matrix(label='Indentified Fish', headers=table_headers, interactive=False, visible=False)
# Button for opening result in annotation editor
annotation_btn = gr.Button("Edit Annotation", visible=False)
annotation_btn.click(prepare_annotation, annotation_btn, [components['annotation_progress'], components['master_tabs']], _js="() => " + str(i))
# Add components to tab dict for easy access later on
tabs.append({
'tab': tab,
'metadata': metadata_out,
'video': video_out,
'table': table_out,
'annotation_btn': annotation_btn
})
# Add all components to list of visualization outputs
visual_components.extend([tab, metadata_out, video_out, table_out, annotation_btn])
components['result_tabs'] = tab_parent
return visual_components |